LLM Fine-Tuning

LLM: Large Language Model

LLM fine-tuning studies how additional training can adapt a pretrained model to new tasks, specialized knowledge, languages, or desired behaviors. Common approaches include supervised fine-tuning on curated or synthetic examples, preference optimization and reinforcement learning, and parameter-efficient methods such as LoRA and QLoRA that update only a small part of the model. Researchers also develop data-selection methods and selective layer or neuron updates to reduce training cost, and evaluate results on settings such as reasoning, code generation, multilingual tasks, and domain-specific question answering. A central challenge is gaining reliable task performance without losing general abilities or safety; results depend on the data, model, and evaluation, and retrieval can sometimes be a better alternative for adding knowledge.

Latest papers

758 papers76 papers in the last four weeks, up 15% on the four weeks before. 0.4% of all new papers.

Assessment of Generative Named Entity Recognition in the Era of Large Language Models
Jan 25, 2026Qi Zhan, Yile Wang, Hui HuangNamed Entity Recognition LLM Information Extraction
College of Computer Science and Software Engineering, Shenzhen University
Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation
Jan 21, 2026Shovito Barua Soumma, Asiful Arefeen, Stephanie M. Carpenter +2Counterfactual Explanations LLM Fine-Tuning
College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA. · School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA. · School of Nutritional Sciences and Wellness, University of Arizona, Tucson, AZ 85721, USA.
Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding
Jan 8, 2026Sungmok Jung, Yeonkyoung So, Joonhak Lee +3LLM Evaluation LLM Fine-Tuning
Graduate School of Data Science, Seoul National University · Dept. of Computer Science and Engineering, Seoul National University
Shuttling Compiler for Trapped-Ion Quantum Computers Based on Fine-Tuned Large Language Models
Dec 19, 2025Fabian Kreppel, Reza Salkhordeh, Ferdinand Schmidt-Kaler +1Quantum Circuit Compilation LLM Fine-Tuning
Institute of Computer Science, Johannes Gutenberg University, Staudingerweg 9, 55128 Mainz, Germany · Institute of Physics, Johannes Gutenberg University, Staudingerweg 7, 55128 Mainz, Germany · Department of Computer Science, Saarland University, Saarland Informatics Campus, E1 1, 66123 Saarbrücken, Germany
Understanding Negative Learning in Large Language Model Fine-tuning
Nov 30, 2025Yue Wang, Qizhou Wang, Zizhuo Zhang +3LLM Fine-Tuning RL Fine-Tuning
TMLR Group, Department of Computer Science, Hong Kong Baptist University · RIKEN AIP · The University of Tokyo
Aligning LLMs with Biomedical Knowledge using Balanced Fine-Tuning
Nov 26, 2025Zhenchao Tang, Fang Wang, Haohuai He +13LLM Alignment LLM Fine-Tuning
Tencent AI for Life Sciences Lab, Shenzhen, China. · Sun Yat-sen University, Shenzhen, China. · The Hong Kong Polytechnic University, Hong Kong SAR, China. +2
Bilevel Data Curation for LLM Fine-tuning: Offline Selection and Online Self-Refining Generation
Nov 26, 2025Quan Xiao, Yutong Xuan, Gaowen Liu +2Supervised Fine-Tuning LLM Self-Refinement
Cornell University · Cisco Research
Fine-Tune, Then Rectify
Nov 23, 2025Zikun Ye, Jinglong Zhao, Lei WangLLM Fine-Tuning
Foster School of Business, University of Washington, Seattle, WA · Questrom School of Business, Boston University, Boston, MA
Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptation
Nov 8, 2025Yinjie Cheng, Paul Youssef, Christin Seifert +2Knowledge Editing LLM Fine-Tuning
School of Computer Science University of Sheffield Sheffield, UK · Mathematics and Computer Science Marburg University Marburg, Germany
AyurParam: A State-of-the-Art Bilingual Language Model for Ayurveda
Nov 4, 2025Sravan Gorugantu, Mohd Nauman, Vijay Devane +7Medical LLMs Healthcare
BharatGen Team
LuxIT: A Luxembourgish Instruction Tuning Dataset from Monolingual Seed Data
Oct 28, 2025Julian Valline, Cedric Lothritz, Siwen Guo +1LLM Fine-Tuning Synthetic Data Generation
Luxembourg Institute of Science and Technology 5 Av. des Hauts-Fourneaux L-4362 Esch-sur-Alzette
Data Provenance Auditing of Fine-Tuned Large Language Models with a Text-Preserving Technique
Oct 7, 2025Yanming Li, Cédric Eichler, Nicolas Anciaux +3Data Provenance Dataset Watermarking
PETSCRAFT · Petscraft, Inria, Palaiseau, France · INSA CVL, Bourges, France +6
Uncovering the Computational Ingredients of Human-Like Representations in LLMs
Oct 1, 2025Zach Studdiford, Timothy T. Rogers, Kushin Mukherjee +1LLM Evaluation LLM Fine-Tuning
University of Wisconsin–Madison · Stanford University
Learning from Convenience Samples: Fine-Tuning LLMs to Predict Voting Choices from Biased Survey Samples
Sep 29, 2025Tobias Holtdirk, Dennis Assenmacher, Arnim Bleier +1LLM Fine-Tuning
LMU Munich · Munich Center for Machine Learning · GESIS - Leibniz Institute for the Social Sciences +2
Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning
Sep 29, 2025Xin Qiu, Yulu Gan, Conor F. Hayes +6Fine-Tuning Evolutionary Optimization
Cognizant AI Lab, San Francisco, CA, USA · Massachusetts Institute of Technology, Cambridge, MA, USA · University of California, Los Angeles, Los Angeles, CA, USA +1
Scale or Reason? A Compute-Equivalent Analysis of Reasoning Distillation
Sep 26, 2025Nicolas Boizard, Hippolyte Gisserot-Boukhlef, Kevin El Haddad +2CoT Distillation LLM Fine-Tuning
1Diabolocom · 5MICS, CentraleSupélec, Université Paris-Saclay · 2Artefact Research Center +2
EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention
Aug 22, 2025Yifan Zhang, Chen Huang, Yueke Zhang +5Code Translation LLM Fine-Tuning
Vanderbilt University1 · National University of Singapore2 · University of Notre Dame3
Dataset Construction for Training LLM to Learn Analog Circuit Knowledge
Aug 14, 2025Zihao Chen, Ji Zhuang, Jinyi Shen +11Language Model Pretraining Knowledge Augmentation for Language Models
Fudan University, Shanghai, China
FlexP-SFT: A Flexible Aggregation-Free Framework for On-Device Personalized Split Federated Fine-Tuning of LLMs
Aug 14, 2025Jiaxiang Geng, Tianjun Yuan, Pengchao Han +3Communication-Efficient Distributed Training LLM Fine-Tuning
Duke Kunshan University, Suzhou, China · Guangdong University of Technology, Guangzhou, China · Jade Bird Fire (JBF), China +1
PLoRA: Efficient Concurrent LoRA Training for Large Language Models
Aug 4, 2025Minghao Yan, Zhuang Wang, Zhen Jia +2LLM Fine-Tuning Efficient Language Model Training
University of Wisconsin-Madison · Amazon Web Services
Using Large Language Models for Legal Decision-Making in Austrian Value-Added Tax Law: A Comparative Study
Jul 11, 2025Marina Luketina, Andrea Benkel, Christoph G. SchuetzAI-Assisted Decision Making Legal Reasoning
University of Applied Sciences Upper Austria – Campus Steyr, Wehrgrabengasse 1–3, Steyr, 4400, Austria · Johannes Kepler University Linz, Altenberger Str. 69, Linz, 4040, Austria
Aligning Language Models with Observational Data: Opportunities and Risks from a Causal Perspective
May 30, 2025Erfan LoghmaniLLM Alignment LLM Fine-Tuning
Foster School of Business University of Washington Seattle, WA 98195
Learning Composable Chains-of-Thought
May 28, 2025Fangcong Yin, Zeyu Leo Liu, Liu Leqi +2LLM Fine-Tuning CoT Reasoning
The University of Texas at Austin · Princeton University
APE: Selective Fine-tuning with Acceptance Criteria for Language Model Adaptation
May 26, 2025Javier MarínLLM Fine-Tuning Selective Fine-Tuning
RECAST: Expanding the Boundaries of LLMs' Complex Instruction Following with Multi-Constraint Data
May 25, 2025Zhengkang Guo, Wenhao Liu, Mingchen Xie +13LLM Fine-Tuning Synthetic Data Generation
College of Computer Science and Artificial Intelligence, Fudan University · Xiaohongshu Inc.
Watch your steps: Dormant Adversarial Behaviors that Activate upon LLM Finetuning
May 22, 2025Thibaud Gloaguen, Mark Vero, Robin Staab +1LLM Backdoor Attacks Backdoor Attacks
ETH Zurich
ZenFlow: Enabling Stall-Free Offloading for LLM Training
May 18, 2025Tingfeng Lan, Yusen Wu, Bin Ma +7LLM Training LLM Fine-Tuning
University of Virginia · University of California, Merced · Argonne National Laboratory +2
Batch Augmentation with Unimodal Fine-tuning for Multimodal Fusion of Large Language Models
May 10, 2025H M Dipu Kabir, Subrota Kumar Mondal, Mohammad Ali MoniMultimodal Fusion Multimodal Classification
AI and Cyber Futures Institute, Charles Sturt University, Australia · Rural Health Research Institute, Charles Sturt University, Australia · School of Computer Science and Engineering, Macau University of Science and Technology, Macao
Evaluating the Scalability and Adversarial Generalization of GRPO-Trained NLI Models
Apr 25, 2025Pablo Miralles-González, Javier Huertas-Tato, Alejandro Martín +1LLM Fine-Tuning RL for Language Model Reasoning
Department of Computer Systems, Technical University of Madrid, Madrid, Spain · United Arab Emirates University, Al Ain, United Arab Emirates
Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks
Apr 24, 2025Yang Liu, Kejia Zhang, Bingjie Yan +11Privacy-Preserving Language Models LLM Fine-Tuning
The Hong Kong Polytechnic University, China · Institute for AI Industry Research, Tsinghua University, China · Institute for AI Industry Research, Tsinghua University, China and Shanghai Jiao Tong University, China +3

Jan 25, 2026J/K move · Enter open · S save

Assessment of Generative Named Entity Recognition in the Era of Large Language Models

Qi Zhan, Yile Wang, Hui Huang

College of Computer Science and Software Engineering, Shenzhen University

Named Entity Recognition LLM Information Extraction LLM Fine-Tuning LLM Evaluation

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Named entity recognition (NER) is evolving from a sequence labeling task into a generative paradigm with the rise of large language models (LLMs). We conduct a systematic evaluation of open-source LLMs on both flat and nested NER tasks. We investigate several research questions including the performance gap between generative NER and traditional NER models, the impact of output formats, whether LLMs rely on memorization, and the preservation of general capabilities after fine-tuning. Through experiments across eight LLMs of varying scales and four standard NER datasets, we find that: (1) With parameter-efficient fine-tuning and structured formats like inline bracketed or XML, open-source LLMs achieve performance competitive with traditional encoder-based models and surpass decoder-based LLMs with in-context learning techniques; (2) The NER capability of LLMs stems from instruction-following and generative power, not mere memorization of entity-label pairs; and (3) Applying NER instruction tuning has minimal impact on general capabilities of LLMs, even improving performance on datasets like DROP by 25.50 to 45.32 F1 points due to enhanced entity understanding. These findings demonstrate that generative NER with LLMs is a promising, user-friendly alternative to traditional methods. We release the data and code at https://github.com/szu-tera/LLMs4NER.